MATLAB and Simulink for Mobile Robots

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MATLAB and Simulink provide algorithms, modeling, and simulation tools, ROS and hardware connectivity for developing autonomous mobile robots (AMRs), service robots, and other unmanned ground vehicles (UGVs).

With MATLAB and Simulink, you can:

Import virtual models of your robot and refine requirements for mechanical design and electrical components
Simulate sensor models for Inertial Navigation Systems and GNSS sensors
Localize your robot using algorithms such as particle filter and Monte Carlo Localization
Build environment maps using Simultaneous Localization and Mapping (SLAM) algorithms
Find optimal paths using path planning algorithms such as A* and RRT
Evaluate path optimality using path metrics such as smoothness and clearance to obstacles
Navigate in dynamic environments using path following and obstacle avoidance control algorithms
Generate production code automatically for target hardware

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Platform Development

Build or import physical models and simulate robot dynamics for wheeled and legged robots. Apply real-world constraints such as contact forces and torques to analyze the effects of your algorithms on the hardware platform. With MATLAB and Simulink, you can:

Use kinematic motion models and wheel encoders for typical mobile robot designs such as differential drive or Ackermann steering
Design detailed dynamic model with physical modeling of the robot’s mechanics
Import mechanical designs from CAD software and connect them with electrical and control systems in a single simulation model
Interface with external robot simulators such as Gazebo for environment modeling
Connect to ROS/ROS2 middleware

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Perception and Localization

Enable robot vision to build environment maps and localize your mobile robot. Develop mapping, localization, and object detection applications using sensor models and prebuilt algorithms so your mobile robot can learn its surroundings and location. With MATLAB and Simulink, you can:

Simulate and fuse IMU and GPS sensor readings for accurate pose estimation
Localize a lidar-based robot using Adaptive Monte Carlo Localization algorithms
Build and visualize 2D and 3D maps using Lidar SLAM or monocular visual SLAM
Improve map accuracy by interactively modifying loop closures using the SLAM Map Builder app
Represent dynamic environments by creating and visualizing egocentric occupancy maps
Detect, identify, and track objects for safe robot navigation using deep learning and machine learning algorithms such as YOLO, SSD, and CNN

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Motion Planning and Control

Find paths for your mobile robot to reach its destination. Generate waypoints and send control commands to follow a global path or a local trajectory. For a mobile robot to navigate in an unknown environment, MATLAB and Simulink provide search and sampling-based planning algorithms and path following control algorithms. With MATLAB and Simulink, your robot can:

Find the shortest and obstacle-free path using algorithms such as A* and RRT
Navigate safely around obstacles in a dynamic environment with local re-planning
Visualize and evaluate the planned path with metrics such as smoothness and distance from obstacles
Optimize the path through nonlinear model predictive control
Follow the planned path using Pure Pursuit Controller
Calculate steering commands to avoid obstacles using Vector Field Histograms
Avoid obstacles using reinforcement learning methods such as DDPG

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Simulation-Based Testing

Detect design errors with simulation and reduce the risk and cost of hardware testing. MATLAB and Simulink provide interactive apps and simulation tools to optimize the performance as well as development and testing time for your mobile robot applications. With MATLAB and Simulink, you can:

Generate trajectories to emulate sensor motions and calibrate their performance
Eliminate error sources of wheel encoders by analyzing the odometry estimate
Use abstract models to quickly validate your autonomous algorithms or build higher fidelity models
Connect with Gazebo to perform time-synchronized co-simulation and test your algorithms in closed-loop simulations
Run navigation algorithms in various edge case scenarios before deploying on the mobile platform
Automatically generate C/C++, VHDL^®/Verilog^®, CUDA^® C/C++ code to be used for rapid-prototyping and production use